- Cancer Systems Biology Center of HoPE (Heterogeneity of Phenotypic Evolution)
Our Cancer Systems Biology Center of HoPE (Heterogeneity of Phenotypic Evolution) focuses on finding effective treatments for resistant tumors by studying the evolution of phenotypes emergent in late-stage breast and ovarian cancer. Our team will develop a suite of systems-based strategies to understand how genomic diversity, clonal evolution, and phenotypic change interact in the progression toward chemoresistant cancer. To evaluate their potential for therapy, we will then test these dynamic models in clinical trials. We hypothesize that acquired resistance emerges from selection acting on phenotypes during tumor evolution, and that simultaneously measuring and modeling subclone genotypes and phenotypes will identify new, and testable, therapeutic targets.
During treatment, the subclones from every patient’s tumor follow unique evolutionary and resistance trajectories. DNA sequencing has revealed significant subclone genotypic diversity within a single tumor, while RNA sequencing has established phenotypic diversity both across and within subclones. This diversity provides the variation required by evolution under the selective pressure created by the tumor microenvironment and treatment. Using computational tools to organize this complex variation, we will develop a new class of dynamical systems models of subclone evolution and acquisition of oncogenic phenotypes during treatment to identify key chemo-resistant cell states using our unique patient cohorts. These mechanistic models will identify points of vulnerability. Our clinical trials will be aimed at blocking transition of tumors to a resistant state by targeting critical resistant phenotypes.
Our Center is comprised of an Administrative, Education/Outreach, Translational, and Computational Cores, in addition to two complementary projects. The synergies are derived from: 1) the merged parameterization of the evolutionary models drawn from deep longitudinal patient progression studies (Project 1) and broad multisite metastatic tumor analyses (Project 2), both resulting in a model to identify resistant states for clinical targeting; and 2) an integrated computational and experimental framework and resources for dissecting tumor heterogeneity and evolution that will contribute to an improved capacity for personalized cancer therapy. Our multidisciplinary team of systems biologists, bioinformaticians, tumor biologists, pharmacologists, mathematical biologists, and clinicians will tackle these scientific challenges. We will create programs to educate the next generation of scientists in systems biology and inform the community about the latest scientific advances and their impact on treatment strategies. We will provide state of the art tools for the analysis of patient samples and tumor genomic complexity. These studies move beyond prior research by integrating cell population dynamics and cellular phenotypes with cellular genotypes, and will deliver approaches and a knowledge base to block or reverse the transition to a resistant state for advanced stage breast and ovarian cancer patients.
Our center is comprised of a multi-disciplinary team with complementary skillsets. Our team shares common goals and interests that have in many cases spanned a decade.
Dr. Andrea Bild
Dr. Andrea Bild trained as a pharmacologist with a specialization in genomics and cancer cell biology. Her research program has established the development and clinical translation of a systems biology framework for personalized medicine genomics. Specifically, her research has enabled 1) investigations of signaling pathways and networks in a physiologically relevant setting: patient tumors; 2) algorithms to personalize matching of effective drugs to patients; and 3) systems-guided clinical trials with novel therapeutic strategies. Dr. Bild has previously worked with every investigator that is part of this team, and has many shared publications and clinical trials with investigators on the grant. Dr. Bild has also developed and founded the Genome Science Program at the University of Utah in order to train students and scientists in genomics and systems biology as well as create a rich collaborative structure for faculty across campus with expertise in this field.
Dr. Fred Adler
Dr. Fred Adler trained as an applied mathematician with specialization in dynamical systems and mathematical ecology, and is Director of the Center for Quantitative Biology. His research program, through his joint appointment in the Departments of Mathematics and Biology, ranges from evolutionary ecology to mathematical oncology. These areas are unified through a commitment to finding key mechanisms in complex systems through mathematical modeling and close interaction with experimentalists and data. He has trained 17 graduate students across this broad range of topics, and his most influential research includes modeling the dynamics of cystic fibrosis to optimize the timing of lung transplantation, the dynamics of populations in spatially subdivided landscapes, and modeling of biodiversity in interacting communities. His expertise in collaboration across disciplinary boundaries, linking dynamical systems with data, and mentoring of trainees will effectively integrate the modeling research and computational core into all aspects of this project.
Professor David Bowtell
Professor David Bowtell is Head of the Cancer Genomics and Genetics Program at the Peter MacCallum Cancer Centre (Melbourne, AU), where he was Director of Research (2000-09) and holds a joint appointment as a Group Leader and Senior Principal Research Fellow at the Garvan Institute for Medical Research (Sydney, AU). He is a Visiting Professor at Dana Farber Harvard Cancer Center (Boston, MA). Prof. Bowtell has an extensive background in human cancer genomics. He is Principal Investigator (PI) for the Australian Ovarian Cancer Study (AOCS), one of the largest population-based cohort studies of ovarian cancer in the world, involving over 3000 women, and CASCADE, a rapid autopsy study. Prof. Bowtell’s research has focused on the classification of ovarian cancer and mechanisms of primary and acquired drug resistance.
Dr. Jeffrey Chang
Dr. Jeffrey Chang is a multidisciplinary cancer genomics researcher specializing in breast cancer, metastasis, and the epithelial-to-mesenchymal transition. He uses a range of approaches, including genomics, systems biology, and cell biology. He has developed novel methods for the analysis of gene expression signatures and next generation sequencing data using computer science and Bayesian statistical methods. In addition to his relevant research experience, Dr. Chang has had over 5 years’ experience as the co-founder and previous co-director of the computational and bioinformatics core at the University of Utah, and was the co-founder and former director of the Biopython project. Dr. Chang will co-direct studies on dissecting clonal structure and functional co-operativity in breast cancer metastasis. He will also direct the Computational Core.
Dr. Adam Cohen is a board certified medical oncologist with a master’s degree in mathematics. His research specializes in clinical trials, genomics, and biomarkers. He has developed and been the PI for three investigator-initiated clinical trials and has been the local PI for many multi-site trials. Dr. Cohen has successfully applied genomic biomarkers in his completed clinical trials, and is the PI for a tissue acquisition protocol that has been used in our successful collection of pleural effusion samples.
Dr. Gabor Marth
Dr. Gabor Marth, Professor of Human Genetics and Co-Director of the USTAR Center for Genetic Discovery, is a computational biologist with a long history of algorithm development for genomic data analysis. He developing sequence analysis tools in the C. elegans and the Human Genome Project, and participated in the SNP Consortium and the International HapMap Project. More recently, he played a leading role in the 1000 Genomes Project (1000GP), developing genomic data standards (SAM/BAM, VCF) that are now de facto standards in genomics. Dr. Marth has established critical variant detection algorithms for detecting somatic cancer mutations and for analyzing tumor tissue heterogeneity at the cellular level.
Dr. Philip Moos was trained as an engineer and cell and molecular biologist. He has used genomics in studies including delineation of patient phenotype segregation and pharmacological effects on cellular systems. He is also a senior administrator for several department and college-wide initiatives in the College of Pharmacy, including a role as Director of Graduate Studies and on the professional program admissions committee. This combination of genomic project expertise and administrative leadership will provide a strong base for his governance of the Translational and Outreach cores for this proposal.
Dr. Sunil Sharma
Dr. Sunil Sharma is Chief of Medical Oncology and an international expert in Drug Development at Huntsman Cancer Institute and University of Utah. Dr. Sharma has successfully directed over 100 clinical trials. He is the Director of Center for Investigational Therapeutics. Dr. Sharma is also an expert on epigenetics and Drug discovery in the epigenetic space. He is an author on more than 100 publications and his laboratory developed SP-2577, the Lysine Specific Demethylase (LSD-1) inhibitor. In addition, his laboratory and clinical trials program (Phase 1 Program) has developed novel therapeutics and biomarkers in the areas of epigenetics and signal transduction pathways.
Dr. Theresa Werner is a board certified medical oncologist who specializes in gynecologic and breast malignancies and clinical trials. Her clinical research program centers around targeted therapy for cancer patients with an emphasis on genomic biomarker driven treatment selection. Dr. Werner also serves as Medical Director of the Clinical Trials Office at Huntsman Cancer Institute and serves as PI on over 40 clinical trials at present. Dr. Werner has been integral in developing an infrastructure at our institution to enable successful complex clinical trials with coordination with radiology, pathology, surgery, pharmacy, and basic scientists.
Overview of our center’s research. Project 1, focused on evolution of tumors over time and approaches for reinstatement of chemosensitivity, will develop dynamic models that measure tumor cell chemo-response states overusing serial collections of patient tumors collected during chemotherapy treatment. Project 2 will identify common driver phenotypes in space, using multi-site metastatic cancer, and find therapeutic regimens targeting cooperative phenotypes in heterogeneous tumors. For both projects, clinical trials will test models for effective reversal of drug resistance evolution.
Project 1. Dynamic genomic and microenvironmental models of chemoresistance
PIs: Bild, Adler, Sharma; co-Is: Werner, Bowtell
Breast and ovarian cancers are heterogeneous diseases, as a typical tumor contains multiple “subclones”, which are defined as evolutionarily related subpopulations of cells with a different complement of somatically acquired DNA mutations and phenotypes. When chemotherapeutic agents are administered to the patient, some of these subclones may gain a selective advantage and develop resistance to the treatment, resulting in cancer relapse and progression. For this reason, it is imperative to identify these subclones and their evolution across treatment; and to understand how the genomic aberrations within these subclones drive resistance to chemotherapy. We will integrate experimental biology and computational models across temporal samples of patient tumors as they develop a resistant state in order to better understand and combat refractory and terminal cancer. To enable the study of tumor heterogeneity evolution in patients, we will utilize a highly unique collection of metastatic tumor cells from breast and ovarian cancer patients before, during, and after treatments, often across multiple courses of chemotherapy, as well as tumors from a clinical trial taken before and after therapy. We use deep sequencing to find genomic aberrations at each of these time points, and develop systems models to identify the subclones and follow phenotypic changes and their functional impacts of subclone evolution in response to chemotherapy. We hypothesize that 1) Dynamical systems models based on the evolution of subclone structure and acquisition of oncogenic phenotypes during treatment can identify key factors in the development of a chemo-resistant state; and 2) We can delay development of a chemo-resistant cancer state by inhibiting development of phenotypes that emerge over time commonly during treatment. We will model resistant cancer cell populations and both extrinsic and immune microenvironmental factors to identify critical features of acquired resistance and apply these models to a clinical trial aimed at blocking transition to a resistant cancer state. While these components can exhibit co-dependencies, by their nature they can also have vulnerabilities based on these interactive features, and if one can inhibit dependent relationships within a population it may be possible to shift the equilibrium of a tumor from a chemoresistant state to a sensitive state. The algorithms and procedures we are developing in this proposal will for a rational basis for real-time patient monitoring and making treatment choices for refractory patients. The outcomes of this research will deliver approaches to block or reverse the transition to a resistant state for advanced stage breast and ovarian cancer patients.
Project 2. Targeting cooperative phenotypes common in spatial heterogeneity
PIs: Chang, Cohen; co-I: Bowtell
Recent studies in primary tumors have found a remarkable degree of intratumor heterogeneity, where a single tumor is comprised of a range of subclones exhibiting a diversity of phenotypes, including molecular profiles, proliferation capacity, and response to therapies. Although heterogeneity is now widely reported, few studies have investigated the heterogeneity of metastatic tumors at the end stage, despite the fact that metastatic cancer is estimated to be responsible for over 90% of cancer deaths. For breast and ovarian cancer, tumors that progress to metastasis are refractory to treatment. Therefore, there is a great need to determine the mechanisms by which subclonal diversity can affect the metastatic phenotype and underlie the difficulties in treatment. Studying metastatic tumors is difficult due to the challenges in collecting patient tissues. While primary tissues are typically obtained through biopsy, this is rarely performed for metastatic sites. To address this difficulty, we have developed both a rapid autopsy strategy where we collect fresh samples of metastatic tumors within hours of patient death, as well as collections of metastatic tumor biopsies in the clinical trial setting prior to and after drug treatment. These collections enable us to profile multiple metastatic sites and investigate the association between metastatic sites and subclonal evolution in an isogenic background. We propose to leverage this unique data set to investigate the relationship between evolution of tumor subclones during metastatic progression and the phenotypic profiles of these tumors. We hypothesize that, despite the diversity in their genetic mutation profiles, metastatic tumors exhibit clonal dynamics that ultimately leads to convergence on more common cooperative phenotypic networks, and that targeting the key dependencies within this network will lead to increased collapse of the metastatic tumor population. To investigate this, we will profile the tumors by whole genome sequencing, whole exome sequencing, and single cell RNA sequencing. This data, coupled with our newly developed algorithms for dissecting subclonal populations using tree reconstruction algorithms, for eliciting phenotypes from gene expression profiles using Bayesian statistics, and for simulating phenotypic evolution using mathematical models from ecology; will enable us to understand (Aim 1) the subclonal heterogeneity that underlies metastatic initiation and progression; (Aim 2) how cooperative functions evolve to a chemo-refractory signaling network, and therapeutic strategies to target it; and (Aim 3) how these dynamics are manifested human tumors in a clinical trial. Our investigations represent the first characterization of the clonal dynamics of a large multisite metastatic cohort, and will provide a new framework for understanding and treating end-stage tumors based on the evolution of cooperative phenotypes. We will develop these models on patient samples and test them in a unique clinical trial, ensuring the physiological, if not clinical, relevance of our findings.
Translational Shared Resource Core (Moos)
The Translational Shared Resource Core will provide services for both research projects. This core is central to the proposal’s mission as it directly facilitates: 1) collection, processing, pharmacological testing and use of samples from our patient cohorts; and 2) generation and collection of DNA- and RNA-sequencing data, including single-cell sequencing. Specifically, we will collect, process, and maintain patient-derived cells for temporal and spatial samples from the patients we profile. In preparation for sequencing, we will enrich for tumor cells, and separate white blood cells and other normal cell types. For single-cell experiments, we will use microfluidics to isolate individual cells, prior to extracting DNA or RNA that will be amplified for sequencing. The core will also be responsible for comparing sequencing results for bulk tumors with results from single-cell sequencing, which will enable us to continue optimizing our single-cell sequencing process and to perform comparisons that help us better understand temporal and spatial tumor evolution, which is central to the success of the scientific projects.
Computational Core (Chang)
The main purpose of the Computational Core is to support the scientific goals of the center by ensuring correct and reproducible analysis of next generation sequencing data. This will be accomplished through five tasks. 1) Data management: we will store and maintain provenance of the raw and pre-processed data in archives that track relevant metadata, such as creation data and checks on secured servers. 2) Data Preprocessing: we will develop standardized approaches to pre-processing the data, and create reference versions of the pre-processed data ready for further analysis. 3) Algorithm Development: We will coordinate with investigators to develop algorithms for the modeling of tumor heterogeneity and its evolution. The Core will maintain the source code in a git repository and will identify stable working versions that are then tagged and archived. 4) Develop Pipelines: to facilitate the processing of the data, and to ensure its reproducibility, we will develop standardized pipelines for the preprocessing and analysis of the data. 5) Standardize Environments: The processing of next generation sequencing data requires a multitude of software programs, each of which can affect the final result. To mitigate variation due to differences in software, versions, or libraries, we will create standardized analysis environments equipped with validated software and libraries and distribute them as Docker containers.
Education and Outreach Core (Moos and Werner)
This core are to build a singular research community within the consortium, educate students and the community on the latest advances in systems biology, and reach out to cancer advocates and patients regarding the impact of systems biology and cancer heterogeneity on treatment strategies to build excitement for future developments. We will sponsor student exchange programs, seminars, and courses on systems biology, as well as participate in organizations that promote health and cancer awareness. We will invite leaders in systems biology—whose expertise and research interests complement and expand our own—to visit our center sites; meet with our faculty, postdocs, and students; and participate in workshops that encourage spirited dialog and broad participation. We will assess the effectiveness of our outreach efforts through surveys that will provide the feedback necessary to ensure we are meeting our goals.
The key to our ongoing collaboration has been communication between groups on our weekly to bi-weekly calls and discussions, focused on coordinated project planning and discussion of experiments and results. The Administrative Core will provide oversight of the Center and will manage its day-to-day operations. Specifically, this Core will: 1) schedule and organize meetings among investigators twice a month; 2) provide monthly and yearly summaries of work accomplished, results, and to-do items; 3) manage the budget; 4) plan internal and external advisory panel meetings and implementation of feedback; 5) manage developmental research project selection and support; and 6) perform administrative tasks related to center management.